7주과제.ipynb

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  "metadata": {
    "colab": {
      "name": "Untitled5.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyP60hyCNpV/X9UFZcdF+l+W",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/juri123123/DALC_Scala/blob/main/7%EC%A3%BC%EA%B3%BC%EC%A0%9C.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UYrDsx_w-DD6"
      },
      "source": [
        "7주차\n",
        "빅데이터 회귀 분석\n",
        "\n",
        "머신러닝\n",
        "- 인공지능의 하위 분야로 분류\n",
        "- 데이터 모델을 구축하는 체계적인 수단\n",
        "- 데이터를 통한 학습 모델을 도출하고 새로운 데이터에 대한 추론과 패턴 예측에 활용\n",
        "\n",
        "- 지도학습(회귀분석) / 비지도학습\n",
        "\n",
        "회귀분석 : 레이블이 연속적인 수치형 데이터일 경우 // 새로운 데이터 레이블을 조사하기 위한 비용과 시간 절감 효\n",
        "분류분석 : 레이블이 범주, 클래스 등 이산적 데이터일 경"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "V8oU_EIPBdbU",
        "outputId": "c4917e89-4004-4c80-9d79-7d423f2aaa46"
      },
      "source": [
        "import seaborn as sns\n",
        "iris = sns.load_dataset('iris')\n",
        "iris.head()"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width species\n",
              "0           5.1          3.5           1.4          0.2  setosa\n",
              "1           4.9          3.0           1.4          0.2  setosa\n",
              "2           4.7          3.2           1.3          0.2  setosa\n",
              "3           4.6          3.1           1.5          0.2  setosa"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "At8QaJUxCwWT"
      },
      "source": [
        "Scikit Learn API 설계 기본 원칙\n",
        "\n",
        "일관성, 검사, 제한된 계층구조, 구성, 합리적 기본\n",
        "\n",
        "- 서로 다른 API를 사용해도 공통 인터페이스를 사용\n",
        "- 넘파이, 판다스와 같은 파이썬 표준 포맷을 사용\n",
        "- 배우기 쉽고 응용이 편리하도록 개발\n",
        "\n",
        "\n",
        "1. 모델 선택\n",
        "2. 인스턴스 생성, 초모수 설정\n",
        "3. 데이터를 특징 배열과 대상 배열로 준비\n",
        "4. 모델 인스턴스의 fit() 메소드를 호출해서 데이터 학\n",
        "5. 정확도를 확인하고 데이터 모델을 적용"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "1W1Cvl6yDcTn",
        "outputId": "964b7e17-e66c-485e-879a-f5fc3e44ebd6"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "mg = np.random.RandomState(42)\n",
        "x = 10 * mg.rand(50)\n",
        "y = 2 * x - 1 + mg.rand(50)\n",
        "plt.scatter(x,y);"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MJjLTe33EDdN"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression #모델 클래스 선택\n",
        "model = LinearRegression(fit_intercept = True) #모델 초모수 선"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ny3qXSRwEWsY",
        "outputId": "7f87a024-920a-4a22-ffd9-75605dfeeefd"
      },
      "source": [
        "X = x[:, np.newaxis] #차원 변경\n",
        "X.shape\n",
        "\n",
        "X = x.reshape(50,1)\n",
        "X.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BuUnsJO2Ehbn",
        "outputId": "51978d95-a1ac-463c-c507-f166a05a0da1"
      },
      "source": [
        "model.fit(X,y)\n",
        "\n",
        "print(model.coef_)\n",
        "print(model.intercept_)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[2.00660766]\n",
            "-0.5350275750800026\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ra1MdTYUEuY-"
      },
      "source": [
        "model.score(X,y) #정확도"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "azSnwDHSExGv",
        "outputId": "2b2a4fa0-4586-4e98-b50b-44621e238dd8"
      },
      "source": [
        "xfit = np.linspace(-1,11)\n",
        "plt.scatter(x,y)\n",
        "plt.plot(xfit, model.coef_ * xfit + model.intercept_, '-r');"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P8qsiGMwffz3"
      },
      "source": [
        "최적의 모델 선택하기\n",
        "편향-분산 트레이드 오프\n",
        "R^2 값: 결정계수, 회귀 모델의 정확도 표현\n",
        "결정계수 1: 완벽 일치, 0: 단순 평균을 구하는 수준\n",
        "다항식의 차수가 높아질수록 복잡도가 높아진다"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M5h1ArPxgQ8y"
      },
      "source": [
        "#파이프라인 \n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.pipeline import make_pipeline\n",
        "\n",
        "def PolynomialRegression(degree=2, **kwargs):\n",
        "  return make_pipeline(PolynomialFeatures(degree), LinearRegression(**kwargs))"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eqfE_yIOgtLp"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "def make_data(N, err=1.0, rseed=1):\n",
        "  rng = np.random.RandomState(rseed)\n",
        "  X = rng.rand(N) **2\n",
        "  y = 10-1./(X+0.1)\n",
        "  if err > 0:\n",
        "    y += err * rng.rand(N)\n",
        "    return X, y\n",
        "\n",
        "  X,y = make_data(40)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "aeJlC2Q9hERe",
        "outputId": "df5d4300-0393-4a9c-f360-6b1780b496a0"
      },
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn; \n",
        "seaborn.set()\n",
        "\n",
        "X_test = np.linspace(-0.1,1.1,500)\n",
        "plt.scatter(X,y,color='black')\n",
        "axix=plt.axis()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 660
        },
        "id": "kDbWpCdShWef",
        "outputId": "1220605c-36cf-4f2e-9cad-c73cf41d8428"
      },
      "source": [
        "plt.scatter(X,y,color='black')\n",
        "\n",
        "for degree in [1,2,3]:\n",
        "  model = PolynomialRegression(degree)\n",
        "  model.fit(X[:,np.newaxis],y)\n",
        "  y_test = model.predict(X_test[:,np.newaxis])\n",
        "  plt.plot(X_test,y_test,label=f'Degree={degree}')\n",
        "\n",
        "plt.axis([-0.1,1.0,-2,12])\n",
        "plt.legend(loc='best')"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-16-88e1af8cd79e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdegree\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m   \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPolynomialRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdegree\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m   \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnewaxis\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m   \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnewaxis\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m   \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mf'Degree={degree}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    348\u001b[0m             \u001b[0mThis\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    349\u001b[0m         \"\"\"\n\u001b[0;32m--> 350\u001b[0;31m         \u001b[0mXt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    351\u001b[0m         with _print_elapsed_time('Pipeline',\n\u001b[1;32m    352\u001b[0m                                  self._log_message(len(self.steps) - 1)):\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36m_fit\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    313\u001b[0m                 \u001b[0mmessage_clsname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Pipeline'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    314\u001b[0m                 \u001b[0mmessage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_log_message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep_idx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 315\u001b[0;31m                 **fit_params_steps[name])\n\u001b[0m\u001b[1;32m    316\u001b[0m             \u001b[0;31m# Replace the transformer of the step with the fitted\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    317\u001b[0m             \u001b[0;31m# transformer. This is necessary when loading the transformer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/joblib/memory.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    350\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    351\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 352\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    353\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    354\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcall_and_shelve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36m_fit_transform_one\u001b[0;34m(transformer, X, y, weight, message_clsname, message, **fit_params)\u001b[0m\n\u001b[1;32m    726\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0m_print_elapsed_time\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage_clsname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    727\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtransformer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'fit_transform'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 728\u001b[0;31m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransformer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    729\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    730\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransformer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    572\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    573\u001b[0m             \u001b[0;31m# fit method of arity 2 (supervised transformation)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 574\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    575\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    576\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_data.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m   1506\u001b[0m         \u001b[0mself\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0minstance\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1507\u001b[0m         \"\"\"\n\u001b[0;32m-> 1508\u001b[0;31m         \u001b[0mn_samples\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_features\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1509\u001b[0m         combinations = self._combinations(n_features, self.degree,\n\u001b[1;32m   1510\u001b[0m                                           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minteraction_only\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    572\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mallow_nd\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    573\u001b[0m             raise ValueError(\"Found array with dim %d. %s expected <= 2.\"\n\u001b[0;32m--> 574\u001b[0;31m                              % (array.ndim, estimator_name))\n\u001b[0m\u001b[1;32m    575\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    576\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Found array with dim 3. Estimator expected <= 2."
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        },
        "id": "MWdgnyuZiBCu",
        "outputId": "958c0397-4277-4653-c8fc-3b3ec4ba34fb"
      },
      "source": [
        "#데이터 검증 곡선\n",
        "from sklearn.model_selection import validation_curve\n",
        "degree = np.arange(1,10)\n",
        "\n",
        "train_score, val_score = validataion_curve(PolynomialRegression(),\n",
        "                                           X[:,np.newaxis],y,'polynomialfeatures__degree',degree,cv=7)\n",
        "plt.plot(degree,np.median(train_score,1),color='blue',label='training score')\n",
        "plt.plot(degree,np.median(val_score,1),color='red',label='validation score')\n",
        "plt.legend(loc='best')\n",
        "plt.ylim(0,1)\n",
        "plt.xlabel('degree');"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-17-3c350dc859b4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mdegree\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m train_score, val_score = validataion_curve(PolynomialRegression(),\n\u001b[0m\u001b[1;32m      6\u001b[0m                                            X[:,np.newaxis],y,'polynomialfeatures__degree',degree,cv=7)\n\u001b[1;32m      7\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdegree\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_score\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'blue'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'training score'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'validataion_curve' is not defined"
          ]
        }
      ]
    }
  ]
}